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Kazuaki Ishizaki 64df08b647 [SPARK-20783][SQL] Create ColumnVector to abstract existing compressed column (batch method) 
## What changes were proposed in this pull request?

This PR abstracts data compressed by `CompressibleColumnAccessor` using `ColumnVector` in batch method. When `ColumnAccessor.decompress` is called, `ColumnVector` will have uncompressed data. This batch decompress does not use `InternalRow` to reduce the number of memory accesses.

As first step of this implementation, this JIRA supports primitive data types. Another PR will support array and other data types.

This implementation decompress data in batch into uncompressed column batch, as rxin suggested at [here](https://github.com/apache/spark/pull/18468#issuecomment-316914076). Another implementation uses adapter approach [as cloud-fan suggested](https://github.com/apache/spark/pull/18468).

## How was this patch tested?

Added test suites

Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>

Closes #18704 from kiszk/SPARK-20783a.
2017-10-04 15:06:44 +08:00
..
catalyst [SPARK-22136][SS] Implement stream-stream outer joins. 2017-10-03 21:42:51 -07:00
core [SPARK-20783][SQL] Create ColumnVector to abstract existing compressed column (batch method) 2017-10-04 15:06:44 +08:00
hive [SPARK-22171][SQL] Describe Table Extended Failed when Table Owner is Empty 2017-10-03 21:27:58 -07:00
hive-thriftserver [SPARK-22087][SPARK-14650][WIP][BUILD][REPL][CORE] Compile Spark REPL for Scala 2.12 + other 2.12 fixes 2017-09-24 09:40:13 +01:00
create-docs.sh [MINOR][DOCS] Minor doc fixes related with doc build and uses script dir in SQL doc gen script 2017-08-26 13:56:24 +09:00
gen-sql-markdown.py [SPARK-21485][FOLLOWUP][SQL][DOCS] Describes examples and arguments separately, and note/since in SQL built-in function documentation 2017-08-05 10:10:56 -07:00
mkdocs.yml [SPARK-21485][SQL][DOCS] Spark SQL documentation generation for built-in functions 2017-07-26 09:38:51 -07:00
README.md [SPARK-21485][SQL][DOCS] Spark SQL documentation generation for built-in functions 2017-07-26 09:38:51 -07:00

README.md

Spark SQL

This module provides support for executing relational queries expressed in either SQL or the DataFrame/Dataset API.

Spark SQL is broken up into four subprojects:

  • Catalyst (sql/catalyst) - An implementation-agnostic framework for manipulating trees of relational operators and expressions.
  • Execution (sql/core) - A query planner / execution engine for translating Catalyst's logical query plans into Spark RDDs. This component also includes a new public interface, SQLContext, that allows users to execute SQL or LINQ statements against existing RDDs and Parquet files.
  • Hive Support (sql/hive) - Includes an extension of SQLContext called HiveContext that allows users to write queries using a subset of HiveQL and access data from a Hive Metastore using Hive SerDes. There are also wrappers that allows users to run queries that include Hive UDFs, UDAFs, and UDTFs.
  • HiveServer and CLI support (sql/hive-thriftserver) - Includes support for the SQL CLI (bin/spark-sql) and a HiveServer2 (for JDBC/ODBC) compatible server.

Running sql/create-docs.sh generates SQL documentation for built-in functions under sql/site.